{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9eal6rHnmHNG"
   },
   "source": [
    "# Question 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wcD3D3yU0i6A"
   },
   "source": [
    "A propriedade de ordenação topológica do algoritmo SOM pode ser usada para formar uma representação bidimensional abstrata para fins de visualização de um espaço de entrada de alta dimensionalidade. O objetivo é visualizar os dados de dimensão 8 em um espaço de dimensão 2, constituído pela grade de neurônios. Para investigar esta forma de representação, considere uma grade bidimensional de neurônios que é treinada tendo como entrada os dados oriundos de quatro distribuições gaussianas, C1, C2, C3, e C4, em um espaço de entrada de dimensionalidade igual a oito, isto é $x = (x_1, x_2, ..., x_8)^t$ . Todas as nuvens têm variâncias unitária, mas centros ou vetores média diferentes dados por $m_1 = (0,0,0,0,0,0,0,0)^t$, $m_2 = (0,0,0,0,0,0,0,0)^t$, $m_3 = (0,0,0,0,0,0,0,0)^t$, $m_4 = (0,0,0,0,0,0,0,0)^t$. Calcule o mapa produzido pelo algoritmo SOM, e verifique como as distribuições dos dados estão representadas. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importando Bibliotecas Necessárias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5m2h1-ot3G9S",
    "outputId": "c05c3d91-225c-4d76-f97a-88d97f5594aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting minisom\n",
      "  Using cached minisom-2.3.5-py3-none-any.whl\n",
      "Installing collected packages: minisom\n",
      "Successfully installed minisom-2.3.5\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install minisom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "MsIh8ShKmDiG"
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "import numpy as np\n",
    "from minisom import MiniSom\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from matplotlib.patches import Rectangle\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import cm, colorbar\n",
    "from matplotlib.lines import Line2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definindo os vetores média passados na questão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "cT80YdKAmeQH"
   },
   "outputs": [],
   "source": [
    "# data\n",
    "m1 = np.zeros(8)\n",
    "m2 = np.array([4,0,0,0,0,0,0,0])\n",
    "m3 = np.array([0,0,0,4,0,0,0,0])\n",
    "m4 = np.array([0,0,0,0,0,0,0,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B9_RDZHz1FxX",
    "outputId": "d9483dc9-92ec-4c29-93cc-9c0fc61623a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [4., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 4., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 4.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# center\n",
    "centers = np.array([m1, m2, m3, m4])\n",
    "centers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gerando dados considerando os centros pré-definidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "dlLfVNGN2x2h"
   },
   "outputs": [],
   "source": [
    "# Gera um conjunto de dados com 1000 amostras distribuídas em centros específicos, com 8 características\n",
    "np.set_printoptions(precision=3)\n",
    "data, y = make_blobs(n_samples=1000, centers=centers, n_features=8, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uAs9mY_vZ9Qf",
    "outputId": "12b611a5-f8b1-48ff-ecc1-22bc863209fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amostra do conjunto de dados\n",
      "\n",
      " [[ 0.819  0.424 -0.966  3.461 -0.467 -1.483 -0.818 -1.134]\n",
      " [-0.102 -0.842  0.751  4.654  0.972 -0.013 -0.6   -1.559]\n",
      " [ 1.52  -2.136  0.76  -0.84   1.372  0.142 -0.673  4.663]\n",
      " [ 1.639  0.908 -0.707  2.21   0.327 -0.835  1.653  6.078]\n",
      " [-0.063  0.241  0.085  4.798  0.924 -1.051  0.869 -1.186]]\n"
     ]
    }
   ],
   "source": [
    "# as primeiras cinco amostras do conjunto de dados\n",
    "print('Amostra do conjunto de dados\\n\\n', data[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inicializando a Rede SOM em grade 10x10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LyCAgRLCZ5j6",
    "outputId": "7508c4f3-a8c4-4228-e7bb-503c951a6a5d"
   },
   "outputs": [],
   "source": [
    "som = MiniSom(\n",
    "    10, 10, 8,  # Dimensões da grade do SOM: 10x10, com vetores de entrada de tamanho 8\n",
    "    sigma=1.5, learning_rate=.7,  # Parâmetros de controle do treinamento\n",
    "    activation_distance='euclidean',  # Distância euclidiana como métrica de ativação\n",
    "    topology='hexagonal',  # Topologia hexagonal para a grade do SOM\n",
    "    neighborhood_function='gaussian'  # Função de vizinhança gaussiana\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Treinamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [ 100 / 100 ] 100% - 0:00:00 left \n",
      " quantization error: 2.2189644220403832\n"
     ]
    }
   ],
   "source": [
    "som.train(data, 100, verbose=True)  # Treina o SOM com 100 iterações, exibindo informações de progresso"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot do resultado da clusterização em grade quadrada 10 x 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "MS9kGd8yZ0M7"
   },
   "outputs": [],
   "source": [
    "xx, yy = som.get_euclidean_coordinates()  # Obtém as coordenadas euclidianas dos neurônios da grade SOM\n",
    "umatrix = som.distance_map()  # Calcula o mapa de distâncias (U-Matrix) para visualização do SOM\n",
    "weights = som.get_weights()  # Obtém os pesos sinápticos dos neurônios da grade SOM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "fyhg6UwmbgI7",
    "outputId": "b7cf8ab1-c306-4d8a-bb40-1eff4e6028cb"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2200x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(22, 15))\n",
    "ax = f.add_subplot(111)\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "# Corrigir para coordenadas quadradas (sem uso de xx e yy)\n",
    "for i in range(weights.shape[0]):\n",
    "    for j in range(weights.shape[1]):\n",
    "        rect = Rectangle(\n",
    "            (i - 0.5, j - 0.5),  # canto inferior esquerdo baseado em i,j\n",
    "            width=1, height=1,\n",
    "            facecolor=cm.Blues(umatrix[i, j]),\n",
    "            alpha=0.4,\n",
    "            edgecolor='gray'\n",
    "        )\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "# Plotagem dos marcadores nas posições corretas\n",
    "markers = ['o', '+', 'x', '^']\n",
    "colors = ['C0', 'C1', 'C2', 'C3']\n",
    "for cnt, x in enumerate(data):\n",
    "    w = som.winner(x)\n",
    "    wx, wy = w[0], w[1]  # usa diretamente as posições da grade\n",
    "    plt.plot(\n",
    "        wx, wy,\n",
    "        markers[y[cnt] - 1],\n",
    "        markerfacecolor='None',\n",
    "        markeredgecolor=colors[y[cnt] - 1],\n",
    "        markersize=12,\n",
    "        markeredgewidth=2\n",
    "    )\n",
    "\n",
    "# Eixos com base em shape da grade\n",
    "plt.xticks(np.arange(weights.shape[0]))\n",
    "plt.yticks(np.arange(weights.shape[1]))\n",
    "\n",
    "# Barra de cores\n",
    "divider = make_axes_locatable(plt.gca())\n",
    "ax_cb = divider.new_horizontal(size=\"5%\", pad=0.05)\n",
    "cb1 = colorbar.ColorbarBase(ax_cb, cmap=cm.Blues, orientation='vertical', alpha=.4)\n",
    "cb1.ax.set_ylabel('Distância dos neurônios na vizinhança', rotation=270, fontsize=18)\n",
    "plt.gcf().add_axes(ax_cb)\n",
    "\n",
    "# Legenda\n",
    "legend_elements = [\n",
    "    Line2D([0], [0], marker='o', color='C0', label='C_1', markerfacecolor='w', markersize=14, linestyle='None', markeredgewidth=2),\n",
    "    Line2D([0], [0], marker='+', color='C1', label='C_2', markerfacecolor='w', markersize=14, linestyle='None', markeredgewidth=2),\n",
    "    Line2D([0], [0], marker='x', color='C2', label='C_3', markerfacecolor='w', markersize=14, linestyle='None', markeredgewidth=2),\n",
    "    Line2D([0], [0], marker='^', color='C3', label='C_4', markerfacecolor='w', markersize=14, linestyle='None', markeredgewidth=2)\n",
    "]\n",
    "\n",
    "ax.set_title('Topologia quadrada da rede SOM', fontsize=24)\n",
    "ax.legend(handles=legend_elements, loc='upper right', ncol=4, fontsize=18)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "dWCApwC2hmqy",
    "9l7K9GMHmWcG"
   ],
   "provenance": []
  },
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
